For experimentation, we adopt three publicly available explainable recommendation datasets, and their data splits [1]}.
During the splitting process, each dataset is randomly divided into training, validation and testing sets with ratio 8:1:1 for 5 times, and the training set holds at least one record for each user and each item.
The three datasets are respectively from TripAdvisor (hotel), Amazon (movies & TV) and Yelp (restaurant).
Each record in the datasets is comprised of a user ID, an item ID, a rating, an explanation, and a feature.
The explanations are sentences extracted from user reviews.
Each explanation contains at least one item feature, e.g., bedroom, which ensures the explanation quality.
Statistics of the datasets are shown in Table REF .
We can see that Yelp is much larger than the other two in terms of size, making it closer to the real-world situation where there are millions of users and items.
<TABLE>